Assignment 2: Doctor’s Aid

BBM103 
Assignment 2: Doctor’s Aid
1. Introduction
A clinical decision support system (CDSS) is a health information technology that
provides clinicians, staff, patients, or other individuals with knowledge and person-specific
information, to help health and health care. CDSS encompasses a variety of tools to enhance
decision-making in the clinical workflow. These tools include automatic alerts and reminders
to care providers and patients, clinical guidelines, condition-specific order sets;’, focused
patient data reports and summaries, documentation templates, diagnostic support, and
contextually relevant reference information, among other tools. Robert Hayward of the
Centre has proposed a working definition for Health Evidence: “Clinical decision support
systems link health observations with health knowledge to influence health choices by
clinicians for improved health care”. CDSSs constitute an essential topic in artificial
intelligence in medicine.
This assignment focuses on a probability-based decision system called Doctor’s Aid for
clinicians.
2. Problem
Worldwide, an estimated 19.3 million new cancer cases and almost 10.0 million
cancer deaths occurred in 2020. Female breast cancer has surpassed lung cancer as the most
commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%). However, after
prevention, early detection and treatment are possible via mammographic screening for
breast cancer. Still, it has limitations, such as overdiagnosis and overtreatment [1].
Overdiagnosis is a critical issue in understanding the potential harms of screening for any
cancer. In addition to causing harm through overtreatment, it labels a person as having
cancer, with all of the psychological, financial, time, and opportunity costs that may entail,
regardless of whether the cancer is treated or surveilled [2].
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3. Doctor’s Aid Scenario
For this aid utility, patient name, diagnosis accuracy, patient’s disease name, disease
incidence, treatment name, and the treatment risk probability will be given in an input file
named “doctors_aid_inputs.txt” an example of this file is provided in the attachment. Note
that the clinician does not have to give the commands in the same order. Each type of
command can be used in any order in the input.
Command types in the input file are as follows:
1. Create a new Patient: The “create” command will add a new patient to the system. For
example, the “create Hayriye, 0.999, Breast Cancer, 50/100000, Surgery, 0.40” command
means that a patient named Hayriye has been diagnosed with breast cancer with a
method that has a 99.9% accuracy ratio. The disease is seen in Turkey in 50 people in
every 100,000 population; this phenomenon is known as incidence. Doctors suggest
surgery as treatment, and the application risk of the surgery is calculated as 40%. The
output of this command will be “Patient Hayriye is recorded.” For another case, the
output will be “Patient Hayriye cannot be recorded due to duplication.”. You do not have
to check for other cases. Please note that the input data are artificially produced. They are
not indicating real-world scenarios.
2. Delete an existing Patient: The “remove” command will remove an existing patient from
the system. For example, “remove Deniz” means the user wants to delete the patient
named Deniz. The output of this command will either be “Patient Deniz is removed.” or
(if there is no such patient) “Patient Deniz cannot be removed due to absence.”.
3. List All Patients with their Information: The “list” command will be used to list the
complete information in the system. For example: “list”
Note that there is no additional operand for this command.
4. Patient’s Probability of having the Disease: The “probability” command will be used to
calculate and show/help the actual fatality probability of a patient. For example, the
“probability Hayriye” command will have an output of “Patient Hayriye has a probability
of 33% of having breast cancer.” This calculation is made according to the information
above in creating part (Yes, it is true!). (See also: FP). The other case is “Probability for
Hayriye cannot be calculated due to absence.”
5. A Patient’s Recommendation for a Particular Treatment: The “recommendation”
command will be used to give a system recommendation to a patient about whether to
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Patient
Name
Diagnosis
Accuracy
Disease Name Disease
Incidence
Treatment Name Treatment
Risk
Hayriye 99.9% Breast Cancer 50/100,000 Surgery 40%
Deniz 99.99% Lung Cancer 40/100,000 Radiotherapy 65%
Toprak 98% Prostate Cancer 21/100,000 Hormonotherapy 20%
have the treatment. As you might notice, the decision is fatally important! For example,
the “recommendation Hayriye” command will result in either “System suggests Hayriye
to have the treatment.” or “System suggests Hayriye NOT to have the treatment.”. The
recommendation calculation will compare the patient’s probability of having the disease
and the treatment risk probability. The above command will result in “System suggests
Hayriye NOT to have the treatment.” in the output file because 40% > 33%. This
assignment will have no confusion, no equal probabilities, or no misspelled inputs. The
other case will be “Recommendation for Hayriye cannot be calculated due to absence.”.
You do not have to check for input correctness. The only case you should check is when a
patient’s name is given (in any command) whether the patient exists in the system.
Also, outputs of each command’s step will be provided to an output file named
“doctors_aid_outputs.txt”.
4. Implementation
Using a multi-dimensional built-in python list as the data structure for the data
in the doctor’s aid is obligatory. For example, example_patient_data_list = [[“Hayriye”,
0.999, “Breast Cancer” “50/100000” “Surgery” 0.40], [“Deniz”, 0.9999, “Lung Cancer”
“40/100000” “Radiotherapy” 0.65]] represents the complete information for a particular
system. Necessary printing operations can be held besides this multi-dimensional list. Deleted
patients will be removed from this list. Names of the patients will be unique, and they can be
used as an identifier for the search operations. Also, you should develop a habit of writing
explanatory comments during your development for clarity.
You will need to implement at least (1) a recommendation function, (2) a disease
probability function, (3) a listing function, (4) a creating new patient function, (5) a removing
patient function, (6) a reading the input file function and (7) a saving to the output file
function within this assignment implementation.
5. What to include in the report:
• Cover (course name, assignment number, title, student number and name-surname, delivery
date)
• Analysis (describe and explain the problem in your own words,
• Design (Write your thoughts on the solution to the problem,
• for each solution/sub-solution/subroutine/function;
• write the prototype of that subroutine and explain what it does,
• write the data structures (arrays) used in that subprogram, explain their purpose,
• write the algorithm (pseudocode) of that program/subroutine.)
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• Programmer's Catalogue (write down the time you spent analyzing, designing,
implementing, testing, and reporting; attach the code and explain the reusability of your
program/subprogram for other programmers, show description of possible function calls.)
• User Catalogue (program's user manual/tutorial, restrictions on the program)
Some example reports are provided as attachments.
6. Grading
In evaluating the assignment, efficiency and compliance with the structural
programming principles will be considered in addition to the correct and complete operation
of the program. It is essential to use several necessary functions because, in computer science,
it is vital to break down a problem into considerable sub-problems.
Why Do We Write Functions?
1. They allow us to conceive of our program as a bunch of sub-steps. (Each sub-step can
be its function. When any program seems too hard, break the overall program into
sub-steps!)
2. They allow us to reuse code instead of rewriting it.
3. Functions allow us to keep our variable namespace clean (local variables only "live" as
long as the function does). In other words, function_1 can use a variable called I, and
function_2 can also use a variable called I, and there is no confusion. Each variable I
only exists when the computer is executing the given function.
4. Functions allow us to test small parts of our program in isolation from the rest. This
aid is especially true in interpreted languages, such as Python and Matlab, but it can
be helpful in C, Java, and ActionScript.
Accordingly, the scoring is as follows:
Evaluation Points Evaluate Yourself / Guess Grading
Indented and Readable Codes 5 …
Using Meaningful Naming 5 …
Using Explanatory Comments 5 …
Efficiency (avoiding unnecessary actions) 5 …
Function Usage 25 …
Correctness 35 …
Report 20 …
There are several negative evaluations … …
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7. Example Program Input/Output Scenario:
*Disease incidence values are up to date for Turkey, according to Globocan 2018. Everything else is imaginary.
*Input and output files are provided in the attachment for further examination. The table has a specific number of tabs for each data instance.
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create Hayriye, 0.999, Breast Cancer, 50/100000, Surgery, 0.40
create Deniz, 0.9999, Lung Cancer, 40/100000, Radiotherapy, 0.50
create Ateş, 0.99, Thyroid Cancer, 16/100000, Chemotherapy, 0.02
probability Hayriye
recommendation Ateş
create Toprak, 0.98, Prostate Cancer, 21/100000, Hormonotherapy, 0.20
create Hypatia, 0.9975, Stomach Cancer, 15/100000, Immunotherapy, 0.04
recommendation Hypatia
create Pakiz, 0.9997, Colon Cancer, 14/100000, Targeted Therapy, 0.30
list
remove Ateş
probability Ateş
recommendation Su
create Su, 0.98, Breast Cancer, 50/100000, Chemotherapy, 0.20
recommendation Su
list
probability Deniz
� doctors_aid_inputs.txt ⤵
Patient Hayriye is recorded.
Patient Deniz is recorded.
Patient Ateş is recorded.
0.33
System suggests Ateş NOT to have the treatment.
Patient Toprak is recorded.
Patient Hypatia is recorded.
System suggests Hypatia to have the treatment.
Patient Pakiz is recorded.
Patient Diagnosis Disease Disease Treatment Treatment
Name Accuracy Name Incidence Name Risk
---------------------------------------------------------------------------------------------
Hayriye 99.9% Breast Cancer 50/100000 Surgery 40%
Deniz 99.99% Lung Cancer 40/100000 Radiotherapy 50%
Ateş 99% Thyroid Cancer 16/100000 Chemotherapy 2%
Toprak 98% Prostate Cancer 21/100000 Hormonotherapy 20%
Hypatia 99.75% Stomach Cancer 15/100000 Immunotherapy 4%
Pakiz 99.97% Colon Cancer 14/100000 Targeted Therapy 30%
Patient Ateş is removed.
Probability for Ateş cannot be calculated due to absence.
Recommendation for Su cannot be calculated due to absence.
Patient Su is saved.
System suggests Su to have the treatment.
Patient Diagnosis Disease Disease Treatment Treatment
Name Accuracy Name Incidence Name Risk
---------------------------------------------------------------------------------------------
Hayriye 99.90% Breast Cancer 50/100000 Surgery 40%
Deniz 99.99% Lung Cancer 40/100000 Radiotherapy 50%
Toprak 98.00% Prostate Cancer 21/100000 Hormonotherapy 20%
Hypatia 99.75% Stomach Cancer 15/100000 Immunotherapy 4%
Pakiz 99.97% Colon Cancer 14/100000 Targeted Therapy 30%
Su 98.00% Breast Cancer 50/100000 Chemotherapy 20%
0.80
� doctors_aid_outputs.txt ⤵
Please note that,
✓ Example input and output files are provided in the attachment for the detailed
examination. Note that punctuation and white spaces in both files are fatally
important. You may lose points because of punctuation or white spaces.
✓ If your program fails to read some of the inputs, your whole assignment could crash at
the beginning!: This indicates no grading.
8. Notes
• Do not use input() function.
• Do not miss the submission deadline.
• Save all your work until the assignment is graded.
• The assignment must be original, individual work. Duplicate or very similar assignments are
both going to be considered as cheating. Show any references or quotes for academic honesty.
• Write READABLE codes and their COMMENTS.
• You can ask your questions via Piazza and you are supposed to be aware of everything
discussed on Piazza.
• You will use online Github Classroom system to submit your work.
• No other submission method (email or etc.) will be accepted. Do not submit any file via email related with this assignment.
• You need to name your assignment as “Assignment2.py” and gather your work into a
single python file.
• Do not forget to fill your grading table with your expected marks, and attach the table inside
your report file. The report is obligatory “Assignment2.pdf ”.
• Do not hesitate to ask questions to your TA. TA’s office hour will be by appointment.
• Do not forget to write down your name and number inside both the python (as
comment) and pdf documents.
• You should write, run and test your code in your local environment before uploading.
• File hierarchy
‣ <Inside your commit without a folder>
‣ Assignment2.py
‣ Assignment2.pdf
‣ doctors_aid_inputs.txt
‣ doctors_aid_outputs.txt
• Sample Run
> python3 Assignment2.py
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9. Policy
All work on assignments must be done individually unless stated otherwise. You are
encouraged to discuss with your classmates about the given assignments, but these discussions
should be carried out abstractly. Discussions about a particular solution to a problem (either in
actual code or pseudocode) will not be tolerated. In short, turning in someone else’s work (from
the internet), in whole or part, as your own will be considered a violation of academic integrity.
Please note that the former condition also holds for the material found on the web, as everything
on the web has been written by someone else.
References for the Academic Integrity (AI):
          https://academicintegrity.ucsd.edu/AI-Handbook-for-UCSD_2019.pdf,
          academicintegrity.org/resources/facts-and-statistics
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